](img/stats_chalkboard.jpeg)
Figure 1: Classroom Stats
Welcome to EDS 222
Statistics is the science of collecting, manipulating, and analyzing empirical data. In this class, we will learn the statistical fundamentals that will enable us to draw conclusions about the environment and its interaction with social and economic systems. We will cover fundamental statistical concepts and tools, and then apply and expand upon those tools to learn some temporal and spatial statistical methods that are particularly helpful in environmental data science. Welcome!
Some concepts we’ll cover:
Sampling and study design, descriptive statistics
Linear and logistic regression (univariate and multivariate)
Hypothesis testing and inference
Spatial weighting, spatial clustering
Time series analysis, forecasting
Instructor
Tamma Carleton (tcarleton@ucsb.edu)
- Office: 3418 Bren Hall
- Office hours: 3:30pm-4:30pm Thursdays, 3526 Bren Hall (Pine Room)
Important links
Weekly course schedule
- Lectures: Tuesdays 2:00pm - 3:15pm (1424 Bren Hall)
- Labs: Thursdays 2:00pm - 3:15pm (1424 Bren Hall)
Learning objectives
The goal of this course is to enable MEDS students to confidently and competently apply statistical tools to environmental and socio-environmental datasets.
- Master fundamental statistical concepts and learn to apply them in
R - Design, conduct, and interpret linear and nonlinear regression models
- Apply spatial statistical methods to environmental data
- Learn about and address common spatial and temporal dependency issues that arise in environmental datasets
Course requirements
Computing
Rversion 4.0.2 (or higher)RStudio version 1.4.1103 (or higher)
Textbook
- Introduction to Modern Statistics, by Mine Çetinkaya-Rundel and Johanna Hardin (link)
Weekly topics [subject to change]
| Week | Lecture topics (Tues) | Lab topics (Thurs) |
|---|---|---|
| 0 (9/25) | No class | Course intro, sampling, study design |
| 1 (10/02) | Data types, summary stats | Summary stats in R |
| 2 (10/09) | Ordinary Least Squares | OLS in R |
| 3 (10/16) | Multiple linear regression | Regression in R, continued |
| 4 (10/23) | Interaction models | Interactions in R |
| 5 (10/30) | Stats in practice: climate change research | Midterm Exam |
| 6 (11/06) | Nonlinear regression models | Logistic regression in R |
| 7 (11/13) | Statistical inference | Hypothesis testing in R |
| 8 (11/20) | Time series in OLS | No class |
| 9 (11/27) | Time series, cont’d, spatial data | Spatial interpolation |
| 10 (12/04) | Kriging in R |
Guest lecture |
| Finals | Final project presentations (Dec 12) | n/a |
This webpage was designed following a template by Dr. Allison Horst.
